Why 96% of Enterprises Face AI Training Data Issues - Dataconomy

#artificialintelligence

A recent survey of over 225 enterprise Data Scientists, AI technologists and business stakeholders involved in active AI and machine learning (ML) projects, suggests that for most organizations, it's still early days for AI technology. The AI market is projected to become a $190 billion industry by 2025 ( according to Markets and Markets), and global spending on cognitive and AI systems is expected to reach $35.8 billion in 2029, an increase of 44.0% over the amount spent in 2018 (according to IDC). This research suggests AI is advanced and on the move, already being undertaken by large enterprises and ready to make an impact on how we live and work. But it is still early days for AI when it comes to the implementation of AI in organisations and there are reasons for that. An AI system requires meticulous training before it can perform its intended function.


Volume and quality of training data are the largest barriers to applying machine learning - Help Net Security

#artificialintelligence

IDC predicts worldwide spending on artificial intelligence (AI) systems will reach $35.8 billion in 2019, and 84% of enterprises believe investing in AI will lead to greater competitive advantages (Statista). However, nearly eight out of 10 enterprise organizations currently engaged in AI and machine learning (ML) report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence, according to Alegion. Data issues are causing enterprises to quickly burn through AI project budgets and face project hurdles. The new report, "Artificial Intelligence and Machine Learning Projects Obstructed by Data Issues" was conducted by Dimensional Research. The findings include feedback from 227 participants including data scientists and business stakeholders involved in active enterprise AI and ML projects, addressing the maturity of ML in the enterprise, today's ML project challenges, and the tools and resources used in these projects.


96% of organizations run into problems with AI and machine learning projects

#artificialintelligence

Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.


Global Big Data Conference

#artificialintelligence

Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.


96% of organizations run into problems with AI and machine learning projects

#artificialintelligence

The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic) The report, conducted by Dimensional Research on behalf of Alegion, surveyed 227 tech professionals who were involved in active AI and machine learning projects.